Overview

Dataset statistics

Number of variables44
Number of observations20000
Missing cells286735
Missing cells (%)32.6%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory6.7 MiB
Average record size in memory352.0 B

Variable types

Numeric39
Categorical5

Alerts

SBP is highly correlated with MAPHigh correlation
MAP is highly correlated with SBP and 1 other fieldsHigh correlation
DBP is highly correlated with MAPHigh correlation
BaseExcess is highly correlated with HCO3 and 1 other fieldsHigh correlation
HCO3 is highly correlated with BaseExcessHigh correlation
pH is highly correlated with BaseExcessHigh correlation
BUN is highly correlated with CreatinineHigh correlation
Creatinine is highly correlated with BUNHigh correlation
Bilirubin_direct is highly correlated with Bilirubin_totalHigh correlation
Bilirubin_total is highly correlated with Bilirubin_directHigh correlation
Hct is highly correlated with HgbHigh correlation
Hgb is highly correlated with HctHigh correlation
Unit1 is highly correlated with Unit2High correlation
Unit2 is highly correlated with Unit1High correlation
ICULOS is highly correlated with HoursHigh correlation
SepsisLabel is highly correlated with SepsisHigh correlation
Sepsis is highly correlated with SepsisLabelHigh correlation
Hours is highly correlated with ICULOSHigh correlation
SBP is highly correlated with MAP and 1 other fieldsHigh correlation
MAP is highly correlated with SBP and 1 other fieldsHigh correlation
DBP is highly correlated with SBP and 1 other fieldsHigh correlation
BaseExcess is highly correlated with HCO3 and 1 other fieldsHigh correlation
HCO3 is highly correlated with BaseExcessHigh correlation
pH is highly correlated with BaseExcessHigh correlation
BUN is highly correlated with Creatinine and 1 other fieldsHigh correlation
Creatinine is highly correlated with BUN and 1 other fieldsHigh correlation
Bilirubin_direct is highly correlated with Bilirubin_totalHigh correlation
Phosphate is highly correlated with BUN and 1 other fieldsHigh correlation
Bilirubin_total is highly correlated with Bilirubin_directHigh correlation
Hct is highly correlated with HgbHigh correlation
Hgb is highly correlated with HctHigh correlation
Unit1 is highly correlated with Unit2High correlation
Unit2 is highly correlated with Unit1High correlation
ICULOS is highly correlated with HoursHigh correlation
SepsisLabel is highly correlated with SepsisHigh correlation
Sepsis is highly correlated with SepsisLabelHigh correlation
Hours is highly correlated with ICULOSHigh correlation
SBP is highly correlated with MAPHigh correlation
MAP is highly correlated with SBP and 1 other fieldsHigh correlation
DBP is highly correlated with MAPHigh correlation
BaseExcess is highly correlated with HCO3High correlation
HCO3 is highly correlated with BaseExcessHigh correlation
BUN is highly correlated with CreatinineHigh correlation
Creatinine is highly correlated with BUNHigh correlation
Bilirubin_direct is highly correlated with Bilirubin_totalHigh correlation
Bilirubin_total is highly correlated with Bilirubin_directHigh correlation
Hct is highly correlated with HgbHigh correlation
Hgb is highly correlated with HctHigh correlation
Unit1 is highly correlated with Unit2High correlation
Unit2 is highly correlated with Unit1High correlation
ICULOS is highly correlated with HoursHigh correlation
SepsisLabel is highly correlated with SepsisHigh correlation
Sepsis is highly correlated with SepsisLabelHigh correlation
Hours is highly correlated with ICULOSHigh correlation
Unit2 is highly correlated with Unit1High correlation
SepsisLabel is highly correlated with SepsisHigh correlation
Sepsis is highly correlated with SepsisLabelHigh correlation
Unit1 is highly correlated with Unit2High correlation
EtCO2 has 16784 (83.9%) missing values Missing
BaseExcess has 19442 (97.2%) missing values Missing
HCO3 has 19584 (97.9%) missing values Missing
FiO2 has 14178 (70.9%) missing values Missing
pH has 14246 (71.2%) missing values Missing
PaCO2 has 14221 (71.1%) missing values Missing
SaO2 has 14875 (74.4%) missing values Missing
AST has 11536 (57.7%) missing values Missing
BUN has 1591 (8.0%) missing values Missing
Alkalinephos has 11530 (57.6%) missing values Missing
Calcium has 1550 (7.8%) missing values Missing
Chloride has 18383 (91.9%) missing values Missing
Creatinine has 1588 (7.9%) missing values Missing
Bilirubin_direct has 18529 (92.6%) missing values Missing
Glucose has 1173 (5.9%) missing values Missing
Lactate has 15240 (76.2%) missing values Missing
Magnesium has 3543 (17.7%) missing values Missing
Phosphate has 8365 (41.8%) missing values Missing
Potassium has 1434 (7.2%) missing values Missing
Bilirubin_total has 11522 (57.6%) missing values Missing
TroponinI has 13436 (67.2%) missing values Missing
Hct has 1953 (9.8%) missing values Missing
Hgb has 1941 (9.7%) missing values Missing
PTT has 15602 (78.0%) missing values Missing
WBC has 2000 (10.0%) missing values Missing
Fibrinogen has 18052 (90.3%) missing values Missing
Platelets has 1992 (10.0%) missing values Missing
Unit1 has 6095 (30.5%) missing values Missing
Unit2 has 6095 (30.5%) missing values Missing
PatientID is uniformly distributed Uniform
PatientID has unique values Unique
HospAdmTime has 1145 (5.7%) zeros Zeros

Reproduction

Analysis started2021-11-29 10:23:46.634623
Analysis finished2021-11-29 10:23:55.725398
Duration9.09 seconds
Software versionpandas-profiling v3.1.1
Download configurationconfig.json

Variables

PatientID
Real number (ℝ≥0)

UNIFORM
UNIQUE

Distinct20000
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean110000.5
Minimum100001
Maximum120000
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size156.4 KiB
2021-11-29T11:23:55.774791image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/

Quantile statistics

Minimum100001
5-th percentile101000.95
Q1105000.75
median110000.5
Q3115000.25
95-th percentile119000.05
Maximum120000
Range19999
Interquartile range (IQR)9999.5

Descriptive statistics

Standard deviation5773.647028
Coefficient of variation (CV)0.05248746167
Kurtosis-1.2
Mean110000.5
Median Absolute Deviation (MAD)5000
Skewness0
Sum2200010000
Variance33335000
MonotonicityStrictly increasing
2021-11-29T11:23:55.880387image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
1000011
 
< 0.1%
1133311
 
< 0.1%
1133381
 
< 0.1%
1133371
 
< 0.1%
1133361
 
< 0.1%
1133351
 
< 0.1%
1133341
 
< 0.1%
1133331
 
< 0.1%
1133321
 
< 0.1%
1133301
 
< 0.1%
Other values (19990)19990
> 99.9%
ValueCountFrequency (%)
1000011
< 0.1%
1000021
< 0.1%
1000031
< 0.1%
1000041
< 0.1%
1000051
< 0.1%
1000061
< 0.1%
1000071
< 0.1%
1000081
< 0.1%
1000091
< 0.1%
1000101
< 0.1%
ValueCountFrequency (%)
1200001
< 0.1%
1199991
< 0.1%
1199981
< 0.1%
1199971
< 0.1%
1199961
< 0.1%
1199951
< 0.1%
1199941
< 0.1%
1199931
< 0.1%
1199921
< 0.1%
1199911
< 0.1%

HR
Real number (ℝ≥0)

Distinct359
Distinct (%)1.8%
Missing4
Missing (%)< 0.1%
Infinite0
Infinite (%)0.0%
Mean82.45575365
Minimum31
Maximum176
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size156.4 KiB
2021-11-29T11:23:55.990360image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/

Quantile statistics

Minimum31
5-th percentile59.5
Q171
median81.5
Q392
95-th percentile109.5
Maximum176
Range145
Interquartile range (IQR)21

Descriptive statistics

Standard deviation15.47201459
Coefficient of variation (CV)0.1876402058
Kurtosis0.2671783352
Mean82.45575365
Median Absolute Deviation (MAD)10.5
Skewness0.4075821578
Sum1648785.25
Variance239.3832353
MonotonicityNot monotonic
2021-11-29T11:23:56.167021image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
80606
 
3.0%
90536
 
2.7%
82475
 
2.4%
70454
 
2.3%
78454
 
2.3%
84449
 
2.2%
74423
 
2.1%
76404
 
2.0%
72396
 
2.0%
86395
 
2.0%
Other values (349)15404
77.0%
ValueCountFrequency (%)
311
 
< 0.1%
31.51
 
< 0.1%
331
 
< 0.1%
361
 
< 0.1%
372
< 0.1%
37.751
 
< 0.1%
382
< 0.1%
391
 
< 0.1%
404
< 0.1%
40.51
 
< 0.1%
ValueCountFrequency (%)
1761
 
< 0.1%
1631
 
< 0.1%
156.51
 
< 0.1%
1561
 
< 0.1%
1551
 
< 0.1%
1471
 
< 0.1%
1462
< 0.1%
1433
< 0.1%
142.52
< 0.1%
1422
< 0.1%

O2Sat
Real number (ℝ≥0)

Distinct70
Distinct (%)0.4%
Missing6
Missing (%)< 0.1%
Infinite0
Infinite (%)0.0%
Mean97.41654996
Minimum62.25
Maximum100
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size156.4 KiB
2021-11-29T11:23:56.270827image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/

Quantile statistics

Minimum62.25
5-th percentile94
Q196
median98
Q399
95-th percentile100
Maximum100
Range37.75
Interquartile range (IQR)3

Descriptive statistics

Standard deviation2.061559043
Coefficient of variation (CV)0.02116230809
Kurtosis17.04957528
Mean97.41654996
Median Absolute Deviation (MAD)1.5
Skewness-1.901720785
Sum1947746.5
Variance4.25002569
MonotonicityNot monotonic
2021-11-29T11:23:56.368301image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
1003296
16.5%
983224
16.1%
972808
14.0%
992536
12.7%
962450
12.2%
951451
7.3%
94681
 
3.4%
97.5441
 
2.2%
98.5391
 
2.0%
96.5389
 
1.9%
Other values (60)2327
11.6%
ValueCountFrequency (%)
62.251
< 0.1%
651
< 0.1%
671
< 0.1%
721
< 0.1%
731
< 0.1%
751
< 0.1%
761
< 0.1%
76.751
< 0.1%
772
< 0.1%
782
< 0.1%
ValueCountFrequency (%)
1003296
16.5%
99.7577
 
0.4%
99.5298
 
1.5%
99.2587
 
0.4%
992536
12.7%
98.7592
 
0.5%
98.5391
 
2.0%
98.2589
 
0.4%
983224
16.1%
97.7598
 
0.5%

Temp
Real number (ℝ≥0)

Distinct219
Distinct (%)1.1%
Missing49
Missing (%)0.2%
Infinite0
Infinite (%)0.0%
Mean36.78687033
Minimum31.4
Maximum40.4
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size156.4 KiB
2021-11-29T11:23:56.473151image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/

Quantile statistics

Minimum31.4
5-th percentile36
Q136.45
median36.7
Q337.1
95-th percentile37.75
Maximum40.4
Range9
Interquartile range (IQR)0.65

Descriptive statistics

Standard deviation0.5584923396
Coefficient of variation (CV)0.01518183892
Kurtosis3.428382447
Mean36.78687033
Median Absolute Deviation (MAD)0.3
Skewness-0.03114273631
Sum733934.85
Variance0.3119136934
MonotonicityNot monotonic
2021-11-29T11:23:56.568953image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
36.61367
 
6.8%
36.51317
 
6.6%
36.81278
 
6.4%
36.71263
 
6.3%
371078
 
5.4%
36.4911
 
4.6%
36.9884
 
4.4%
37.1742
 
3.7%
36.3709
 
3.5%
37.2678
 
3.4%
Other values (209)9724
48.6%
ValueCountFrequency (%)
31.41
 
< 0.1%
32.12
< 0.1%
32.82
< 0.1%
32.952
< 0.1%
332
< 0.1%
33.051
 
< 0.1%
33.11
 
< 0.1%
33.23
< 0.1%
33.252
< 0.1%
33.351
 
< 0.1%
ValueCountFrequency (%)
40.41
 
< 0.1%
40.11
 
< 0.1%
402
< 0.1%
39.51
 
< 0.1%
39.41
 
< 0.1%
39.31
 
< 0.1%
39.251
 
< 0.1%
39.21
 
< 0.1%
39.14
< 0.1%
39.051
 
< 0.1%

SBP
Real number (ℝ≥0)

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION

Distinct485
Distinct (%)2.4%
Missing24
Missing (%)0.1%
Infinite0
Infinite (%)0.0%
Mean126.0335653
Minimum51
Maximum207
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size156.4 KiB
2021-11-29T11:23:56.667560image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/

Quantile statistics

Minimum51
5-th percentile98
Q1112
median124.5
Q3139
95-th percentile160
Maximum207
Range156
Interquartile range (IQR)27

Descriptive statistics

Standard deviation19.12587448
Coefficient of variation (CV)0.1517522291
Kurtosis-0.0662913949
Mean126.0335653
Median Absolute Deviation (MAD)13.5
Skewness0.3496266683
Sum2517646.5
Variance365.7990745
MonotonicityNot monotonic
2021-11-29T11:23:56.765463image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
116331
 
1.7%
118316
 
1.6%
112313
 
1.6%
132312
 
1.6%
120306
 
1.5%
126304
 
1.5%
110301
 
1.5%
114298
 
1.5%
124298
 
1.5%
128297
 
1.5%
Other values (475)16900
84.5%
ValueCountFrequency (%)
511
< 0.1%
51.51
< 0.1%
54.51
< 0.1%
621
< 0.1%
631
< 0.1%
651
< 0.1%
65.51
< 0.1%
662
< 0.1%
66.51
< 0.1%
68.51
< 0.1%
ValueCountFrequency (%)
2071
 
< 0.1%
2031
 
< 0.1%
2021
 
< 0.1%
1981
 
< 0.1%
1971
 
< 0.1%
196.51
 
< 0.1%
195.51
 
< 0.1%
1953
< 0.1%
194.752
< 0.1%
1944
< 0.1%

MAP
Real number (ℝ≥0)

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION

Distinct322
Distinct (%)1.6%
Missing102
Missing (%)0.5%
Infinite0
Infinite (%)0.0%
Mean86.18930295
Minimum33
Maximum144
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size156.4 KiB
2021-11-29T11:23:56.869340image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/

Quantile statistics

Minimum33
5-th percentile68
Q176.5
median84.75
Q394
95-th percentile110.5
Maximum144
Range111
Interquartile range (IQR)17.5

Descriptive statistics

Standard deviation13.16411582
Coefficient of variation (CV)0.1527349146
Kurtosis0.3658354042
Mean86.18930295
Median Absolute Deviation (MAD)8.75
Skewness0.5997528945
Sum1714994.75
Variance173.2939454
MonotonicityNot monotonic
2021-11-29T11:23:56.964775image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
78558
 
2.8%
82540
 
2.7%
80504
 
2.5%
76501
 
2.5%
88497
 
2.5%
86488
 
2.4%
77464
 
2.3%
84455
 
2.3%
74432
 
2.2%
90420
 
2.1%
Other values (312)15039
75.2%
ValueCountFrequency (%)
331
 
< 0.1%
341
 
< 0.1%
43.51
 
< 0.1%
453
< 0.1%
48.51
 
< 0.1%
491
 
< 0.1%
49.751
 
< 0.1%
503
< 0.1%
512
< 0.1%
51.51
 
< 0.1%
ValueCountFrequency (%)
1441
 
< 0.1%
143.51
 
< 0.1%
1431
 
< 0.1%
1411
 
< 0.1%
1404
< 0.1%
139.51
 
< 0.1%
139.251
 
< 0.1%
138.52
< 0.1%
1382
< 0.1%
1372
< 0.1%

DBP
Real number (ℝ≥0)

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION

Distinct325
Distinct (%)1.6%
Missing27
Missing (%)0.1%
Infinite0
Infinite (%)0.0%
Mean65.98379412
Minimum24
Maximum117
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size156.4 KiB
2021-11-29T11:23:57.065735image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/

Quantile statistics

Minimum24
5-th percentile50
Q158
median65
Q373
95-th percentile86
Maximum117
Range93
Interquartile range (IQR)15

Descriptive statistics

Standard deviation11.11804128
Coefficient of variation (CV)0.1684965442
Kurtosis0.4183439062
Mean65.98379412
Median Absolute Deviation (MAD)7
Skewness0.5133707969
Sum1317894.32
Variance123.610842
MonotonicityNot monotonic
2021-11-29T11:23:57.167885image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
58664
 
3.3%
60628
 
3.1%
64609
 
3.0%
62585
 
2.9%
56576
 
2.9%
63554
 
2.8%
65540
 
2.7%
59538
 
2.7%
66528
 
2.6%
61504
 
2.5%
Other values (315)14247
71.2%
ValueCountFrequency (%)
241
 
< 0.1%
27.51
 
< 0.1%
281
 
< 0.1%
28.51
 
< 0.1%
293
< 0.1%
323
< 0.1%
332
< 0.1%
33.52
< 0.1%
344
< 0.1%
353
< 0.1%
ValueCountFrequency (%)
1171
 
< 0.1%
1161
 
< 0.1%
114.751
 
< 0.1%
1131
 
< 0.1%
1123
< 0.1%
1111
 
< 0.1%
110.51
 
< 0.1%
1101
 
< 0.1%
109.751
 
< 0.1%
109.251
 
< 0.1%

Resp
Real number (ℝ≥0)

Distinct121
Distinct (%)0.6%
Missing43
Missing (%)0.2%
Infinite0
Infinite (%)0.0%
Mean18.3046049
Minimum1
Maximum56
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size156.4 KiB
2021-11-29T11:23:57.267472image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile14
Q116
median18
Q320
95-th percentile24
Maximum56
Range55
Interquartile range (IQR)4

Descriptive statistics

Standard deviation3.25810626
Coefficient of variation (CV)0.1779938042
Kurtosis6.330757325
Mean18.3046049
Median Absolute Deviation (MAD)2
Skewness0.5389213854
Sum365305
Variance10.6152564
MonotonicityNot monotonic
2021-11-29T11:23:57.355739image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
183990
20.0%
202754
13.8%
162564
12.8%
171321
 
6.6%
191138
 
5.7%
15995
 
5.0%
22932
 
4.7%
14673
 
3.4%
21661
 
3.3%
24352
 
1.8%
Other values (111)4577
22.9%
ValueCountFrequency (%)
18
 
< 0.1%
1.53
 
< 0.1%
224
0.1%
2.53
 
< 0.1%
312
0.1%
3.253
 
< 0.1%
3.54
 
< 0.1%
3.751
 
< 0.1%
411
0.1%
4.53
 
< 0.1%
ValueCountFrequency (%)
562
< 0.1%
51.51
< 0.1%
402
< 0.1%
391
< 0.1%
382
< 0.1%
371
< 0.1%
36.251
< 0.1%
362
< 0.1%
35.52
< 0.1%
35.251
< 0.1%

EtCO2
Real number (ℝ≥0)

MISSING

Distinct173
Distinct (%)5.4%
Missing16784
Missing (%)83.9%
Infinite0
Infinite (%)0.0%
Mean33.04485386
Minimum10
Maximum100
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size156.4 KiB
2021-11-29T11:23:57.525835image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/

Quantile statistics

Minimum10
5-th percentile17.5
Q128
median33
Q337
95-th percentile43.5
Maximum100
Range90
Interquartile range (IQR)9

Descriptive statistics

Standard deviation10.32476853
Coefficient of variation (CV)0.3124470931
Kurtosis16.13061293
Mean33.04485386
Median Absolute Deviation (MAD)4.5
Skewness2.621801772
Sum106272.25
Variance106.6008452
MonotonicityNot monotonic
2021-11-29T11:23:57.621852image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
35137
 
0.7%
34136
 
0.7%
33136
 
0.7%
32131
 
0.7%
30124
 
0.6%
36112
 
0.6%
29109
 
0.5%
3998
 
0.5%
2896
 
0.5%
3795
 
0.5%
Other values (163)2042
 
10.2%
(Missing)16784
83.9%
ValueCountFrequency (%)
108
< 0.1%
10.56
 
< 0.1%
1110
0.1%
11.254
 
< 0.1%
11.56
 
< 0.1%
1215
0.1%
12.55
 
< 0.1%
1310
0.1%
13.252
 
< 0.1%
13.56
 
< 0.1%
ValueCountFrequency (%)
1007
< 0.1%
994
< 0.1%
98.51
 
< 0.1%
986
< 0.1%
97.52
 
< 0.1%
977
< 0.1%
962
 
< 0.1%
951
 
< 0.1%
942
 
< 0.1%
932
 
< 0.1%

BaseExcess
Real number (ℝ)

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
MISSING

Distinct272
Distinct (%)48.7%
Missing19442
Missing (%)97.2%
Infinite0
Infinite (%)0.0%
Mean-2.538620072
Minimum-22.1
Maximum9.1
Zeros6
Zeros (%)< 0.1%
Negative429
Negative (%)2.1%
Memory size156.4 KiB
2021-11-29T11:23:57.720988image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/

Quantile statistics

Minimum-22.1
5-th percentile-9.26875
Q1-4.6
median-2.4
Q3-0.375
95-th percentile3.88
Maximum9.1
Range31.2
Interquartile range (IQR)4.225

Descriptive statistics

Standard deviation3.886708635
Coefficient of variation (CV)-1.531032028
Kurtosis2.211538927
Mean-2.538620072
Median Absolute Deviation (MAD)2.1
Skewness-0.4712354538
Sum-1416.55
Variance15.10650402
MonotonicityNot monotonic
2021-11-29T11:23:57.814002image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
-1.711
 
0.1%
-2.89
 
< 0.1%
-3.98
 
< 0.1%
-1.38
 
< 0.1%
-37
 
< 0.1%
-3.27
 
< 0.1%
-3.47
 
< 0.1%
06
 
< 0.1%
-3.76
 
< 0.1%
-0.96
 
< 0.1%
Other values (262)483
 
2.4%
(Missing)19442
97.2%
ValueCountFrequency (%)
-22.11
< 0.1%
-18.251
< 0.1%
-16.31
< 0.1%
-15.11
< 0.1%
-15.0751
< 0.1%
-14.051
< 0.1%
-14.0251
< 0.1%
-13.051
< 0.1%
-12.21
< 0.1%
-11.81
< 0.1%
ValueCountFrequency (%)
9.11
< 0.1%
91
< 0.1%
8.51
< 0.1%
8.1751
< 0.1%
7.62
< 0.1%
7.11
< 0.1%
6.31
< 0.1%
6.11
< 0.1%
5.91
< 0.1%
5.851
< 0.1%

HCO3
Real number (ℝ≥0)

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
MISSING

Distinct217
Distinct (%)52.2%
Missing19584
Missing (%)97.9%
Infinite0
Infinite (%)0.0%
Mean23.33960337
Minimum7.7
Maximum34
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size156.4 KiB
2021-11-29T11:23:57.910694image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/

Quantile statistics

Minimum7.7
5-th percentile18.5
Q121.6
median23.475
Q325.1
95-th percentile28.525
Maximum34
Range26.3
Interquartile range (IQR)3.5

Descriptive statistics

Standard deviation3.099274851
Coefficient of variation (CV)0.1327903822
Kurtosis2.193542379
Mean23.33960337
Median Absolute Deviation (MAD)1.725
Skewness-0.2167493007
Sum9709.275
Variance9.605504601
MonotonicityNot monotonic
2021-11-29T11:23:58.007738image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
23.59
 
< 0.1%
22.67
 
< 0.1%
21.56
 
< 0.1%
23.86
 
< 0.1%
23.76
 
< 0.1%
25.36
 
< 0.1%
25.15
 
< 0.1%
24.25
 
< 0.1%
23.25
 
< 0.1%
23.95
 
< 0.1%
Other values (207)356
 
1.8%
(Missing)19584
97.9%
ValueCountFrequency (%)
7.71
< 0.1%
13.11
< 0.1%
13.151
< 0.1%
14.61
< 0.1%
15.31
< 0.1%
16.051
< 0.1%
16.31
< 0.1%
16.41
< 0.1%
16.81
< 0.1%
172
< 0.1%
ValueCountFrequency (%)
341
< 0.1%
32.81
< 0.1%
32.41
< 0.1%
322
< 0.1%
30.91
< 0.1%
30.61
< 0.1%
30.11
< 0.1%
29.71
< 0.1%
29.52
< 0.1%
29.451
< 0.1%

FiO2
Real number (ℝ≥0)

MISSING

Distinct123
Distinct (%)2.1%
Missing14178
Missing (%)70.9%
Infinite0
Infinite (%)0.0%
Mean0.4753126074
Minimum0.04
Maximum2
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size156.4 KiB
2021-11-29T11:23:58.104986image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/

Quantile statistics

Minimum0.04
5-th percentile0.21
Q10.36
median0.4
Q30.5
95-th percentile1
Maximum2
Range1.96
Interquartile range (IQR)0.14

Descriptive statistics

Standard deviation0.2105588808
Coefficient of variation (CV)0.4429903131
Kurtosis2.727652453
Mean0.4753126074
Median Absolute Deviation (MAD)0.1
Skewness1.362236754
Sum2767.27
Variance0.04433504227
MonotonicityNot monotonic
2021-11-29T11:23:58.207509image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0.41596
 
8.0%
0.5824
 
4.1%
0.21608
 
3.0%
1402
 
2.0%
0.7284
 
1.4%
0.45261
 
1.3%
0.6240
 
1.2%
0.28223
 
1.1%
0.3220
 
1.1%
0.35141
 
0.7%
Other values (113)1023
 
5.1%
(Missing)14178
70.9%
ValueCountFrequency (%)
0.042
 
< 0.1%
0.055
 
< 0.1%
0.065
 
< 0.1%
0.1151
 
< 0.1%
0.21608
3.0%
0.211
 
< 0.1%
0.2414
 
0.1%
0.24517
 
0.1%
0.2511
 
0.1%
0.266
 
< 0.1%
ValueCountFrequency (%)
24
 
< 0.1%
1.61
 
< 0.1%
1.52
 
< 0.1%
1.21
 
< 0.1%
1402
2.0%
0.981
 
< 0.1%
0.961
 
< 0.1%
0.957
 
< 0.1%
0.932
 
< 0.1%
0.922
 
< 0.1%

pH
Real number (ℝ≥0)

HIGH CORRELATION
HIGH CORRELATION
MISSING

Distinct135
Distinct (%)2.3%
Missing14246
Missing (%)71.2%
Infinite0
Infinite (%)0.0%
Mean7.379348279
Minimum6.78
Maximum7.615
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size156.4 KiB
2021-11-29T11:23:58.317224image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/

Quantile statistics

Minimum6.78
5-th percentile7.27
Q17.34
median7.38
Q37.42
95-th percentile7.49
Maximum7.615
Range0.835
Interquartile range (IQR)0.08

Descriptive statistics

Standard deviation0.06923347547
Coefficient of variation (CV)0.009382058259
Kurtosis3.550569169
Mean7.379348279
Median Absolute Deviation (MAD)0.04
Skewness-0.6617156254
Sum42460.77
Variance0.004793274126
MonotonicityNot monotonic
2021-11-29T11:23:58.413882image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
7.38342
 
1.7%
7.4321
 
1.6%
7.36289
 
1.4%
7.37273
 
1.4%
7.42268
 
1.3%
7.35261
 
1.3%
7.41254
 
1.3%
7.39253
 
1.3%
7.34250
 
1.2%
7.32217
 
1.1%
Other values (125)3026
 
15.1%
(Missing)14246
71.2%
ValueCountFrequency (%)
6.781
< 0.1%
6.811
< 0.1%
6.941
< 0.1%
6.981
< 0.1%
7.011
< 0.1%
7.021
< 0.1%
7.0251
< 0.1%
7.0551
< 0.1%
7.061
< 0.1%
7.0651
< 0.1%
ValueCountFrequency (%)
7.6151
 
< 0.1%
7.611
 
< 0.1%
7.64
< 0.1%
7.5951
 
< 0.1%
7.593
 
< 0.1%
7.5851
 
< 0.1%
7.584
< 0.1%
7.5751
 
< 0.1%
7.576
< 0.1%
7.569
< 0.1%

PaCO2
Real number (ℝ≥0)

MISSING

Distinct580
Distinct (%)10.0%
Missing14221
Missing (%)71.1%
Infinite0
Infinite (%)0.0%
Mean40.24149074
Minimum12
Maximum100
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size156.4 KiB
2021-11-29T11:23:58.514647image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/

Quantile statistics

Minimum12
5-th percentile28.3
Q135
median39
Q343.5
95-th percentile57
Maximum100
Range88
Interquartile range (IQR)8.5

Descriptive statistics

Standard deviation9.553163238
Coefficient of variation (CV)0.2373958584
Kurtosis6.580025868
Mean40.24149074
Median Absolute Deviation (MAD)4
Skewness1.881281469
Sum232555.575
Variance91.26292786
MonotonicityNot monotonic
2021-11-29T11:23:58.609069image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
38252
 
1.3%
36224
 
1.1%
40221
 
1.1%
37209
 
1.0%
35203
 
1.0%
34201
 
1.0%
39189
 
0.9%
42189
 
0.9%
41176
 
0.9%
44147
 
0.7%
Other values (570)3768
 
18.8%
(Missing)14221
71.1%
ValueCountFrequency (%)
121
 
< 0.1%
153
< 0.1%
15.31
 
< 0.1%
15.51
 
< 0.1%
163
< 0.1%
16.71
 
< 0.1%
174
< 0.1%
184
< 0.1%
18.81
 
< 0.1%
195
< 0.1%
ValueCountFrequency (%)
1001
< 0.1%
982
< 0.1%
951
< 0.1%
941
< 0.1%
93.41
< 0.1%
92.52
< 0.1%
921
< 0.1%
902
< 0.1%
89.151
< 0.1%
891
< 0.1%

SaO2
Real number (ℝ≥0)

MISSING

Distinct377
Distinct (%)7.4%
Missing14875
Missing (%)74.4%
Infinite0
Infinite (%)0.0%
Mean96.81781463
Minimum50.3
Maximum100
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size156.4 KiB
2021-11-29T11:23:58.705419image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/

Quantile statistics

Minimum50.3
5-th percentile92.1
Q195.95
median97.55
Q398.7
95-th percentile99.5
Maximum100
Range49.7
Interquartile range (IQR)2.75

Descriptive statistics

Standard deviation3.080118287
Coefficient of variation (CV)0.03181354897
Kurtosis40.31261174
Mean96.81781463
Median Absolute Deviation (MAD)1.25
Skewness-4.542755846
Sum496191.3
Variance9.487128664
MonotonicityNot monotonic
2021-11-29T11:23:58.805264image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
98.8118
 
0.6%
99.2109
 
0.5%
99106
 
0.5%
98.6105
 
0.5%
99.1105
 
0.5%
98.9104
 
0.5%
98.4102
 
0.5%
98.5101
 
0.5%
98.3100
 
0.5%
98.797
 
0.5%
Other values (367)4078
 
20.4%
(Missing)14875
74.4%
ValueCountFrequency (%)
50.31
< 0.1%
52.51
< 0.1%
58.81
< 0.1%
65.11
< 0.1%
65.61
< 0.1%
66.11
< 0.1%
681
< 0.1%
68.21
< 0.1%
68.851
< 0.1%
71.21
< 0.1%
ValueCountFrequency (%)
1001
 
< 0.1%
99.931
0.2%
99.852
 
< 0.1%
99.82
 
< 0.1%
99.843
0.2%
99.754
 
< 0.1%
99.755
0.3%
99.73
 
< 0.1%
99.6512
 
0.1%
99.6252
 
< 0.1%

AST
Real number (ℝ≥0)

MISSING

Distinct801
Distinct (%)9.5%
Missing11536
Missing (%)57.7%
Infinite0
Infinite (%)0.0%
Mean99.08066517
Minimum5
Maximum9264
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size156.4 KiB
2021-11-29T11:23:58.978054image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/

Quantile statistics

Minimum5
5-th percentile13
Q119
median28
Q352.5
95-th percentile283
Maximum9264
Range9259
Interquartile range (IQR)33.5

Descriptive statistics

Standard deviation403.3067974
Coefficient of variation (CV)4.070489401
Kurtosis173.4224384
Mean99.08066517
Median Absolute Deviation (MAD)11.5
Skewness11.78378329
Sum838618.75
Variance162656.3728
MonotonicityNot monotonic
2021-11-29T11:23:59.076640image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
19273
 
1.4%
18267
 
1.3%
17262
 
1.3%
21259
 
1.3%
20255
 
1.3%
16239
 
1.2%
22228
 
1.1%
15226
 
1.1%
24222
 
1.1%
14210
 
1.1%
Other values (791)6023
30.1%
(Missing)11536
57.7%
ValueCountFrequency (%)
52
 
< 0.1%
5.51
 
< 0.1%
65
 
< 0.1%
6.52
 
< 0.1%
77
 
< 0.1%
814
 
0.1%
8.251
 
< 0.1%
8.56
 
< 0.1%
935
0.2%
9.512
 
0.1%
ValueCountFrequency (%)
92641
< 0.1%
79381
< 0.1%
79061
< 0.1%
7416.51
< 0.1%
73941
< 0.1%
68461
< 0.1%
6602.51
< 0.1%
65601
< 0.1%
60581
< 0.1%
5866.51
< 0.1%

BUN
Real number (ℝ≥0)

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
MISSING

Distinct305
Distinct (%)1.7%
Missing1591
Missing (%)8.0%
Infinite0
Infinite (%)0.0%
Mean21.38318214
Minimum1
Maximum202.5
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size156.4 KiB
2021-11-29T11:23:59.177921image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile6
Q111
median16
Q325
95-th percentile56.8
Maximum202.5
Range201.5
Interquartile range (IQR)14

Descriptive statistics

Standard deviation17.61743484
Coefficient of variation (CV)0.8238921002
Kurtosis10.74599209
Mean21.38318214
Median Absolute Deviation (MAD)6
Skewness2.731324231
Sum393643
Variance310.3740104
MonotonicityNot monotonic
2021-11-29T11:23:59.280338image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
12798
 
4.0%
13790
 
4.0%
14746
 
3.7%
10732
 
3.7%
11731
 
3.7%
15636
 
3.2%
9628
 
3.1%
16615
 
3.1%
17593
 
3.0%
18525
 
2.6%
Other values (295)11615
58.1%
(Missing)1591
 
8.0%
ValueCountFrequency (%)
112
 
0.1%
1.252
 
< 0.1%
1.57
 
< 0.1%
222
 
0.1%
2.58
 
< 0.1%
379
0.4%
3.528
 
0.1%
3.751
 
< 0.1%
4159
0.8%
4.251
 
< 0.1%
ValueCountFrequency (%)
202.51
< 0.1%
2001
< 0.1%
1821
< 0.1%
179.51
< 0.1%
1702
< 0.1%
1611
< 0.1%
1521
< 0.1%
1511
< 0.1%
1471
< 0.1%
1451
< 0.1%

Alkalinephos
Real number (ℝ≥0)

MISSING

Distinct624
Distinct (%)7.4%
Missing11530
Missing (%)57.6%
Infinite0
Infinite (%)0.0%
Mean88.18476978
Minimum11
Maximum1650
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size156.4 KiB
2021-11-29T11:23:59.386208image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/

Quantile statistics

Minimum11
5-th percentile34
Q152
median69
Q396
95-th percentile198
Maximum1650
Range1639
Interquartile range (IQR)44

Descriptive statistics

Standard deviation80.47511024
Coefficient of variation (CV)0.9125737976
Kurtosis74.56907729
Mean88.18476978
Median Absolute Deviation (MAD)20
Skewness6.638995858
Sum746925
Variance6476.243368
MonotonicityNot monotonic
2021-11-29T11:23:59.482635image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
53126
 
0.6%
69124
 
0.6%
58124
 
0.6%
52121
 
0.6%
55119
 
0.6%
56117
 
0.6%
61115
 
0.6%
54115
 
0.6%
51113
 
0.6%
68113
 
0.6%
Other values (614)7283
36.4%
(Missing)11530
57.6%
ValueCountFrequency (%)
112
 
< 0.1%
131
 
< 0.1%
141
 
< 0.1%
151
 
< 0.1%
162
 
< 0.1%
185
< 0.1%
195
< 0.1%
203
 
< 0.1%
2110
0.1%
21.51
 
< 0.1%
ValueCountFrequency (%)
16502
< 0.1%
1303.51
< 0.1%
12141
< 0.1%
11291
< 0.1%
10721
< 0.1%
1051.51
< 0.1%
9722
< 0.1%
959.51
< 0.1%
9581
< 0.1%
9331
< 0.1%

Calcium
Real number (ℝ≥0)

MISSING

Distinct1009
Distinct (%)5.5%
Missing1550
Missing (%)7.8%
Infinite0
Infinite (%)0.0%
Mean7.791532249
Minimum1.01
Maximum27.45
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size156.4 KiB
2021-11-29T11:23:59.583579image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/

Quantile statistics

Minimum1.01
5-th percentile1.33
Q17.8
median8.3
Q38.8
95-th percentile9.4
Maximum27.45
Range26.44
Interquartile range (IQR)1

Descriptive statistics

Standard deviation2.053533862
Coefficient of variation (CV)0.2635596948
Kurtosis5.568138851
Mean7.791532249
Median Absolute Deviation (MAD)0.5
Skewness-1.587741001
Sum143753.77
Variance4.217001322
MonotonicityNot monotonic
2021-11-29T11:23:59.682299image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
8.3845
 
4.2%
8.5837
 
4.2%
8.2762
 
3.8%
8.6754
 
3.8%
8.8741
 
3.7%
8.7740
 
3.7%
8.4726
 
3.6%
8.1669
 
3.3%
8606
 
3.0%
8.9575
 
2.9%
Other values (999)11195
56.0%
(Missing)1550
 
7.8%
ValueCountFrequency (%)
1.011
 
< 0.1%
1.043
 
< 0.1%
1.051
 
< 0.1%
1.0551
 
< 0.1%
1.061
 
< 0.1%
1.076
< 0.1%
1.0752
 
< 0.1%
1.086
< 0.1%
1.0852
 
< 0.1%
1.0913
0.1%
ValueCountFrequency (%)
27.451
 
< 0.1%
25.21
 
< 0.1%
23.71
 
< 0.1%
19.11
 
< 0.1%
18.82
< 0.1%
18.63
< 0.1%
18.31
 
< 0.1%
18.23
< 0.1%
18.11
 
< 0.1%
182
< 0.1%

Chloride
Real number (ℝ≥0)

MISSING

Distinct86
Distinct (%)5.3%
Missing18383
Missing (%)91.9%
Infinite0
Infinite (%)0.0%
Mean105.9225417
Minimum74
Maximum124
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size156.4 KiB
2021-11-29T11:23:59.777929image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/

Quantile statistics

Minimum74
5-th percentile97
Q1103.5
median106
Q3109
95-th percentile113
Maximum124
Range50
Interquartile range (IQR)5.5

Descriptive statistics

Standard deviation5.065038683
Coefficient of variation (CV)0.04781832649
Kurtosis3.110997013
Mean105.9225417
Median Absolute Deviation (MAD)3
Skewness-0.8143374426
Sum171276.75
Variance25.65461686
MonotonicityNot monotonic
2021-11-29T11:23:59.876263image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
107144
 
0.7%
108128
 
0.6%
106126
 
0.6%
105125
 
0.6%
109104
 
0.5%
10490
 
0.4%
10388
 
0.4%
11082
 
0.4%
10261
 
0.3%
11156
 
0.3%
Other values (76)613
 
3.1%
(Missing)18383
91.9%
ValueCountFrequency (%)
741
 
< 0.1%
811
 
< 0.1%
821
 
< 0.1%
82.51
 
< 0.1%
863
< 0.1%
871
 
< 0.1%
884
< 0.1%
893
< 0.1%
89.51
 
< 0.1%
902
< 0.1%
ValueCountFrequency (%)
1243
< 0.1%
1232
 
< 0.1%
1222
 
< 0.1%
1192
 
< 0.1%
118.51
 
< 0.1%
1185
< 0.1%
117.52
 
< 0.1%
1176
< 0.1%
116.52
 
< 0.1%
1162
 
< 0.1%

Creatinine
Real number (ℝ≥0)

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
MISSING

Distinct1807
Distinct (%)9.8%
Missing1588
Missing (%)7.9%
Infinite0
Infinite (%)0.0%
Mean1.573319846
Minimum0.2
Maximum25.33
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size156.4 KiB
2021-11-29T11:23:59.972667image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/

Quantile statistics

Minimum0.2
5-th percentile0.525
Q10.735
median0.94
Q31.35
95-th percentile5.68
Maximum25.33
Range25.13
Interquartile range (IQR)0.615

Descriptive statistics

Standard deviation2.082281524
Coefficient of variation (CV)1.323495366
Kurtosis24.73944754
Mean1.573319846
Median Absolute Deviation (MAD)0.255
Skewness4.412836616
Sum28967.965
Variance4.335896347
MonotonicityNot monotonic
2021-11-29T11:24:00.068906image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0.8208
 
1.0%
0.77204
 
1.0%
0.81197
 
1.0%
0.79190
 
0.9%
0.73187
 
0.9%
0.71186
 
0.9%
0.7184
 
0.9%
0.78180
 
0.9%
0.75178
 
0.9%
0.76172
 
0.9%
Other values (1797)16526
82.6%
(Missing)1588
 
7.9%
ValueCountFrequency (%)
0.21
 
< 0.1%
0.2051
 
< 0.1%
0.221
 
< 0.1%
0.2351
 
< 0.1%
0.241
 
< 0.1%
0.271
 
< 0.1%
0.281
 
< 0.1%
0.2851
 
< 0.1%
0.333
0.2%
0.3052
 
< 0.1%
ValueCountFrequency (%)
25.331
< 0.1%
252
< 0.1%
23.831
< 0.1%
23.8251
< 0.1%
23.4951
< 0.1%
23.4851
< 0.1%
23.371
< 0.1%
21.311
< 0.1%
21.181
< 0.1%
20.291
< 0.1%

Bilirubin_direct
Real number (ℝ≥0)

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
MISSING

Distinct193
Distinct (%)13.1%
Missing18529
Missing (%)92.6%
Infinite0
Infinite (%)0.0%
Mean0.7139055065
Minimum0.01
Maximum23.3
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size156.4 KiB
2021-11-29T11:24:00.162718image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/

Quantile statistics

Minimum0.01
5-th percentile0.1
Q10.1
median0.2
Q30.405
95-th percentile2.48
Maximum23.3
Range23.29
Interquartile range (IQR)0.305

Descriptive statistics

Standard deviation2.00545946
Coefficient of variation (CV)2.809138523
Kurtosis59.33609311
Mean0.7139055065
Median Absolute Deviation (MAD)0.1
Skewness7.099006483
Sum1050.155
Variance4.021867645
MonotonicityNot monotonic
2021-11-29T11:24:00.255939image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0.1432
 
2.2%
0.2265
 
1.3%
0.3119
 
0.6%
0.484
 
0.4%
0.536
 
0.2%
0.628
 
0.1%
121
 
0.1%
0.720
 
0.1%
0.2517
 
0.1%
0.813
 
0.1%
Other values (183)436
 
2.2%
(Missing)18529
92.6%
ValueCountFrequency (%)
0.015
< 0.1%
0.024
< 0.1%
0.035
< 0.1%
0.044
< 0.1%
0.051
 
< 0.1%
0.054
< 0.1%
0.066
< 0.1%
0.061
 
< 0.1%
0.077
< 0.1%
0.084
< 0.1%
ValueCountFrequency (%)
23.31
< 0.1%
21.3351
< 0.1%
20.571
< 0.1%
202
< 0.1%
19.8151
< 0.1%
18.3951
< 0.1%
17.951
< 0.1%
16.61
< 0.1%
15.61
< 0.1%
151
< 0.1%

Glucose
Real number (ℝ≥0)

MISSING

Distinct714
Distinct (%)3.8%
Missing1173
Missing (%)5.9%
Infinite0
Infinite (%)0.0%
Mean128.1403591
Minimum44
Maximum446
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size156.4 KiB
2021-11-29T11:24:00.425536image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/

Quantile statistics

Minimum44
5-th percentile87
Q1104.5
median122
Q3140.5
95-th percentile196.5
Maximum446
Range402
Interquartile range (IQR)36

Descriptive statistics

Standard deviation35.65520072
Coefficient of variation (CV)0.2782511379
Kurtosis6.506633846
Mean128.1403591
Median Absolute Deviation (MAD)18
Skewness1.887041391
Sum2412498.54
Variance1271.293339
MonotonicityNot monotonic
2021-11-29T11:24:00.521058image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
126223
 
1.1%
115213
 
1.1%
118212
 
1.1%
122212
 
1.1%
108210
 
1.1%
127206
 
1.0%
121206
 
1.0%
116206
 
1.0%
123205
 
1.0%
114202
 
1.0%
Other values (704)16732
83.7%
(Missing)1173
 
5.9%
ValueCountFrequency (%)
441
< 0.1%
501
< 0.1%
511
< 0.1%
592
< 0.1%
611
< 0.1%
61.51
< 0.1%
622
< 0.1%
631
< 0.1%
642
< 0.1%
65.251
< 0.1%
ValueCountFrequency (%)
4461
< 0.1%
4452
< 0.1%
4291
< 0.1%
424.51
< 0.1%
401.51
< 0.1%
3891
< 0.1%
384.51
< 0.1%
3841
< 0.1%
3811
< 0.1%
376.51
< 0.1%

Lactate
Real number (ℝ≥0)

MISSING

Distinct1082
Distinct (%)22.7%
Missing15240
Missing (%)76.2%
Infinite0
Infinite (%)0.0%
Mean2.232431723
Minimum0.5
Maximum22.25
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size156.4 KiB
2021-11-29T11:24:00.624626image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/

Quantile statistics

Minimum0.5
5-th percentile0.84
Q11.24
median1.7
Q32.5
95-th percentile5.33075
Maximum22.25
Range21.75
Interquartile range (IQR)1.26

Descriptive statistics

Standard deviation1.83628181
Coefficient of variation (CV)0.8225478033
Kurtosis22.78210679
Mean2.232431723
Median Absolute Deviation (MAD)0.55
Skewness3.919380805
Sum10626.375
Variance3.371930884
MonotonicityNot monotonic
2021-11-29T11:24:00.716087image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
1.145
 
0.2%
1.241
 
0.2%
1.438
 
0.2%
1.336
 
0.2%
134
 
0.2%
0.930
 
0.1%
1.530
 
0.1%
1.0829
 
0.1%
1.4229
 
0.1%
1.3529
 
0.1%
Other values (1072)4419
 
22.1%
(Missing)15240
76.2%
ValueCountFrequency (%)
0.54
< 0.1%
0.541
 
< 0.1%
0.551
 
< 0.1%
0.562
 
< 0.1%
0.574
< 0.1%
0.591
 
< 0.1%
0.68
< 0.1%
0.612
 
< 0.1%
0.6151
 
< 0.1%
0.621
 
< 0.1%
ValueCountFrequency (%)
22.251
< 0.1%
19.3451
< 0.1%
19.121
< 0.1%
17.751
< 0.1%
17.421
< 0.1%
16.971
< 0.1%
16.961
< 0.1%
16.8951
< 0.1%
161
< 0.1%
15.941
< 0.1%

Magnesium
Real number (ℝ≥0)

MISSING

Distinct117
Distinct (%)0.7%
Missing3543
Missing (%)17.7%
Infinite0
Infinite (%)0.0%
Mean2.024313362
Minimum0.5
Maximum8.4
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size156.4 KiB
2021-11-29T11:24:00.811351image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/

Quantile statistics

Minimum0.5
5-th percentile1.6
Q11.85
median2
Q32.2
95-th percentile2.5
Maximum8.4
Range7.9
Interquartile range (IQR)0.35

Descriptive statistics

Standard deviation0.3229414811
Coefficient of variation (CV)0.1595313686
Kurtosis25.67020394
Mean2.024313362
Median Absolute Deviation (MAD)0.15
Skewness2.517298682
Sum33314.125
Variance0.1042912002
MonotonicityNot monotonic
2021-11-29T11:24:00.908826image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
22241
11.2%
1.91917
9.6%
2.11816
 
9.1%
1.81350
 
6.8%
2.21276
 
6.4%
1.7859
 
4.3%
2.3836
 
4.2%
2.05613
 
3.1%
1.6505
 
2.5%
1.95466
 
2.3%
Other values (107)4578
22.9%
(Missing)3543
17.7%
ValueCountFrequency (%)
0.51
 
< 0.1%
0.651
 
< 0.1%
0.92
 
< 0.1%
15
 
< 0.1%
1.051
 
< 0.1%
1.117
0.1%
1.152
 
< 0.1%
1.230
0.1%
1.21
 
< 0.1%
1.254
 
< 0.1%
ValueCountFrequency (%)
8.41
< 0.1%
7.11
< 0.1%
6.351
< 0.1%
6.21
< 0.1%
5.71
< 0.1%
5.41
< 0.1%
5.31
< 0.1%
5.21
< 0.1%
5.151
< 0.1%
51
< 0.1%

Phosphate
Real number (ℝ≥0)

HIGH CORRELATION
MISSING

Distinct284
Distinct (%)2.4%
Missing8365
Missing (%)41.8%
Infinite0
Infinite (%)0.0%
Mean3.51783627
Minimum0.6
Maximum12
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size156.4 KiB
2021-11-29T11:24:01.008859image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/

Quantile statistics

Minimum0.6
5-th percentile2
Q12.75
median3.3
Q34
95-th percentile5.9
Maximum12
Range11.4
Interquartile range (IQR)1.25

Descriptive statistics

Standard deviation1.251250763
Coefficient of variation (CV)0.3556876066
Kurtosis5.73622451
Mean3.51783627
Median Absolute Deviation (MAD)0.6
Skewness1.725705724
Sum40930.025
Variance1.565628473
MonotonicityNot monotonic
2021-11-29T11:24:01.109788image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
3.2407
 
2.0%
3.5395
 
2.0%
3394
 
2.0%
3.3392
 
2.0%
3.4390
 
1.9%
2.9368
 
1.8%
2.8364
 
1.8%
3.1351
 
1.8%
3.7328
 
1.6%
2.7327
 
1.6%
Other values (274)7919
39.6%
(Missing)8365
41.8%
ValueCountFrequency (%)
0.64
 
< 0.1%
0.651
 
< 0.1%
0.72
 
< 0.1%
0.751
 
< 0.1%
0.87
< 0.1%
0.851
 
< 0.1%
0.92
 
< 0.1%
116
0.1%
1.110
0.1%
1.214
0.1%
ValueCountFrequency (%)
127
< 0.1%
11.851
 
< 0.1%
11.81
 
< 0.1%
11.61
 
< 0.1%
11.51
 
< 0.1%
11.42
 
< 0.1%
11.31
 
< 0.1%
11.21
 
< 0.1%
111
 
< 0.1%
10.91
 
< 0.1%

Potassium
Real number (ℝ≥0)

MISSING

Distinct451
Distinct (%)2.4%
Missing1434
Missing (%)7.2%
Infinite0
Infinite (%)0.0%
Mean4.045369493
Minimum1.575
Maximum9.8
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size156.4 KiB
2021-11-29T11:24:01.210169image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/

Quantile statistics

Minimum1.575
5-th percentile3.35
Q13.7
median4
Q34.3
95-th percentile4.9
Maximum9.8
Range8.225
Interquartile range (IQR)0.6

Descriptive statistics

Standard deviation0.5023039386
Coefficient of variation (CV)0.1241676291
Kurtosis6.93451772
Mean4.045369493
Median Absolute Deviation (MAD)0.3
Skewness1.326775252
Sum75106.33
Variance0.2523092467
MonotonicityNot monotonic
2021-11-29T11:24:01.303828image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
41399
 
7.0%
3.91289
 
6.4%
3.81273
 
6.4%
4.11258
 
6.3%
3.71095
 
5.5%
4.2881
 
4.4%
3.6803
 
4.0%
4.3720
 
3.6%
4.4645
 
3.2%
3.5613
 
3.1%
Other values (441)8590
43.0%
(Missing)1434
 
7.2%
ValueCountFrequency (%)
1.5751
 
< 0.1%
2.252
 
< 0.1%
2.32
 
< 0.1%
2.351
 
< 0.1%
2.41
 
< 0.1%
2.57
< 0.1%
2.552
 
< 0.1%
2.68
< 0.1%
2.651
 
< 0.1%
2.712
0.1%
ValueCountFrequency (%)
9.81
 
< 0.1%
9.42
< 0.1%
9.22
< 0.1%
8.21
 
< 0.1%
7.91
 
< 0.1%
7.81
 
< 0.1%
7.63
< 0.1%
7.5951
 
< 0.1%
7.41
 
< 0.1%
7.351
 
< 0.1%

Bilirubin_total
Real number (ℝ≥0)

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
MISSING

Distinct286
Distinct (%)3.4%
Missing11522
Missing (%)57.6%
Infinite0
Infinite (%)0.0%
Mean1.249097665
Minimum0.1
Maximum49.2
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size156.4 KiB
2021-11-29T11:24:01.399756image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/

Quantile statistics

Minimum0.1
5-th percentile0.3
Q10.5
median0.8
Q31.2
95-th percentile3.1
Maximum49.2
Range49.1
Interquartile range (IQR)0.7

Descriptive statistics

Standard deviation2.329118511
Coefficient of variation (CV)1.864640834
Kurtosis126.2752191
Mean1.249097665
Median Absolute Deviation (MAD)0.3
Skewness9.704517087
Sum10589.85
Variance5.42479304
MonotonicityNot monotonic
2021-11-29T11:24:01.498924image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0.6794
 
4.0%
0.5779
 
3.9%
0.7760
 
3.8%
0.4693
 
3.5%
0.8623
 
3.1%
0.9504
 
2.5%
1409
 
2.0%
0.3374
 
1.9%
1.1320
 
1.6%
1.2254
 
1.3%
Other values (276)2968
 
14.8%
(Missing)11522
57.6%
ValueCountFrequency (%)
0.132
 
0.2%
0.151
 
< 0.1%
0.152
 
< 0.1%
0.2153
 
0.8%
0.2519
 
0.1%
0.3374
1.9%
0.314
 
0.1%
0.3553
 
0.3%
0.4693
3.5%
0.4251
 
< 0.1%
ValueCountFrequency (%)
49.21
< 0.1%
46.41
< 0.1%
43.31
< 0.1%
41.61
< 0.1%
36.851
< 0.1%
36.451
< 0.1%
35.41
< 0.1%
34.651
< 0.1%
31.21
< 0.1%
30.21
< 0.1%

TroponinI
Real number (ℝ≥0)

MISSING

Distinct1372
Distinct (%)20.9%
Missing13436
Missing (%)67.2%
Infinite0
Infinite (%)0.0%
Mean5.093911487
Minimum0.01
Maximum440
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size156.4 KiB
2021-11-29T11:24:01.598893image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/

Quantile statistics

Minimum0.01
5-th percentile0.01
Q10.03
median0.075
Q30.8
95-th percentile32.0455
Maximum440
Range439.99
Interquartile range (IQR)0.77

Descriptive statistics

Standard deviation19.44571104
Coefficient of variation (CV)3.81744188
Kurtosis88.01086808
Mean5.093911487
Median Absolute Deviation (MAD)0.065
Skewness7.757148428
Sum33436.435
Variance378.135678
MonotonicityNot monotonic
2021-11-29T11:24:01.691971image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0.011238
 
6.2%
0.03805
 
4.0%
0.02290
 
1.5%
0.04256
 
1.3%
0.05176
 
0.9%
0.06144
 
0.7%
0.07134
 
0.7%
0.08100
 
0.5%
0.0995
 
0.5%
0.181
 
0.4%
Other values (1362)3245
 
16.2%
(Missing)13436
67.2%
ValueCountFrequency (%)
0.011238
6.2%
0.01552
 
0.3%
0.02290
 
1.5%
0.02533
 
0.2%
0.03805
4.0%
0.033
 
< 0.1%
0.0351
 
< 0.1%
0.03527
 
0.1%
0.04256
 
1.3%
0.04536
 
0.2%
ValueCountFrequency (%)
4401
 
< 0.1%
297.051
 
< 0.1%
20012
0.1%
199.531
 
< 0.1%
194.0951
 
< 0.1%
193.441
 
< 0.1%
190.041
 
< 0.1%
185.2151
 
< 0.1%
183.51
 
< 0.1%
180.081
 
< 0.1%

Hct
Real number (ℝ≥0)

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
MISSING

Distinct917
Distinct (%)5.1%
Missing1953
Missing (%)9.8%
Infinite0
Infinite (%)0.0%
Mean32.49576135
Minimum9.3
Maximum65
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size156.4 KiB
2021-11-29T11:24:01.865903image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/

Quantile statistics

Minimum9.3
5-th percentile23.1
Q127.65
median32.3
Q336.95
95-th percentile42.885
Maximum65
Range55.7
Interquartile range (IQR)9.3

Descriptive statistics

Standard deviation6.19351436
Coefficient of variation (CV)0.19059453
Kurtosis-0.2796348538
Mean32.49576135
Median Absolute Deviation (MAD)4.65
Skewness0.2665834415
Sum586451.005
Variance38.35962013
MonotonicityNot monotonic
2021-11-29T11:24:01.958204image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
35103
 
0.5%
33.5101
 
0.5%
3496
 
0.5%
35.595
 
0.5%
33.386
 
0.4%
29.686
 
0.4%
34.285
 
0.4%
33.285
 
0.4%
31.585
 
0.4%
30.184
 
0.4%
Other values (907)17141
85.7%
(Missing)1953
 
9.8%
ValueCountFrequency (%)
9.31
< 0.1%
11.31
< 0.1%
13.31
< 0.1%
13.551
< 0.1%
14.61
< 0.1%
15.51
< 0.1%
15.751
< 0.1%
161
< 0.1%
16.31
< 0.1%
16.61
< 0.1%
ValueCountFrequency (%)
651
< 0.1%
64.21
< 0.1%
64.11
< 0.1%
59.851
< 0.1%
59.61
< 0.1%
58.151
< 0.1%
57.11
< 0.1%
56.31
< 0.1%
55.61
< 0.1%
55.21
< 0.1%

Hgb
Real number (ℝ≥0)

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
MISSING

Distinct398
Distinct (%)2.2%
Missing1941
Missing (%)9.7%
Infinite0
Infinite (%)0.0%
Mean10.6649042
Minimum2.6
Maximum26.6
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size156.4 KiB
2021-11-29T11:24:02.053845image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/

Quantile statistics

Minimum2.6
5-th percentile7.55
Q19
median10.5
Q312.15
95-th percentile14.4
Maximum26.6
Range24
Interquartile range (IQR)3.15

Descriptive statistics

Standard deviation2.147157958
Coefficient of variation (CV)0.2013293244
Kurtosis0.3215379227
Mean10.6649042
Median Absolute Deviation (MAD)1.6
Skewness0.4779463755
Sum192597.505
Variance4.610287296
MonotonicityNot monotonic
2021-11-29T11:24:02.152313image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
11.3259
 
1.3%
10.8247
 
1.2%
9.3246
 
1.2%
10.5232
 
1.2%
9.5231
 
1.2%
9.2231
 
1.2%
8.5230
 
1.1%
10.6227
 
1.1%
11226
 
1.1%
10.3226
 
1.1%
Other values (388)15704
78.5%
(Missing)1941
 
9.7%
ValueCountFrequency (%)
2.61
< 0.1%
41
< 0.1%
4.31
< 0.1%
4.51
< 0.1%
4.81
< 0.1%
4.91
< 0.1%
51
< 0.1%
5.11
< 0.1%
5.22
< 0.1%
5.351
< 0.1%
ValueCountFrequency (%)
26.61
< 0.1%
24.82
< 0.1%
23.81
< 0.1%
23.51
< 0.1%
22.51
< 0.1%
21.61
< 0.1%
21.21
< 0.1%
211
< 0.1%
20.61
< 0.1%
20.551
< 0.1%

PTT
Real number (ℝ≥0)

MISSING

Distinct925
Distinct (%)21.0%
Missing15602
Missing (%)78.0%
Infinite0
Infinite (%)0.0%
Mean38.87937131
Minimum20
Maximum250
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size156.4 KiB
2021-11-29T11:24:02.247499image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/

Quantile statistics

Minimum20
5-th percentile24
Q128.05
median31.4125
Q337.6
95-th percentile79.765
Maximum250
Range230
Interquartile range (IQR)9.55

Descriptive statistics

Standard deviation26.84004152
Coefficient of variation (CV)0.6903414489
Kurtosis28.13037137
Mean38.87937131
Median Absolute Deviation (MAD)4.1125
Skewness4.797294311
Sum170991.475
Variance720.3878287
MonotonicityNot monotonic
2021-11-29T11:24:02.345356image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
28.938
 
0.2%
28.637
 
0.2%
27.337
 
0.2%
30.436
 
0.2%
30.835
 
0.2%
30.735
 
0.2%
2835
 
0.2%
29.535
 
0.2%
28.834
 
0.2%
29.634
 
0.2%
Other values (915)4042
 
20.2%
(Missing)15602
78.0%
ValueCountFrequency (%)
2021
0.1%
20.13
 
< 0.1%
20.31
 
< 0.1%
20.43
 
< 0.1%
20.52
 
< 0.1%
20.62
 
< 0.1%
20.71
 
< 0.1%
20.84
 
< 0.1%
20.96
 
< 0.1%
214
 
< 0.1%
ValueCountFrequency (%)
2503
 
< 0.1%
249.95
 
< 0.1%
24916
0.1%
237.51
 
< 0.1%
235.51
 
< 0.1%
224.41
 
< 0.1%
212.31
 
< 0.1%
210.61
 
< 0.1%
2061
 
< 0.1%
204.91
 
< 0.1%

WBC
Real number (ℝ≥0)

MISSING

Distinct868
Distinct (%)4.8%
Missing2000
Missing (%)10.0%
Infinite0
Infinite (%)0.0%
Mean10.27482528
Minimum0.1
Maximum302
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size156.4 KiB
2021-11-29T11:24:02.442327image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/

Quantile statistics

Minimum0.1
5-th percentile4.4
Q17
median9.4
Q312.3
95-th percentile18.5
Maximum302
Range301.9
Interquartile range (IQR)5.3

Descriptive statistics

Standard deviation6.378892202
Coefficient of variation (CV)0.6208273162
Kurtosis412.8860134
Mean10.27482528
Median Absolute Deviation (MAD)2.6
Skewness13.38156119
Sum184946.855
Variance40.69026572
MonotonicityNot monotonic
2021-11-29T11:24:02.536905image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
8.8188
 
0.9%
7.4170
 
0.9%
9.8165
 
0.8%
10161
 
0.8%
8.6159
 
0.8%
7.6152
 
0.8%
8151
 
0.8%
7.1150
 
0.8%
8.2149
 
0.7%
8.4148
 
0.7%
Other values (858)16407
82.0%
(Missing)2000
 
10.0%
ValueCountFrequency (%)
0.110
0.1%
0.23
 
< 0.1%
0.33
 
< 0.1%
0.31
 
< 0.1%
0.46
< 0.1%
0.51
 
< 0.1%
0.62
 
< 0.1%
0.751
 
< 0.1%
0.84
 
< 0.1%
0.91
 
< 0.1%
ValueCountFrequency (%)
3021
< 0.1%
200.81
< 0.1%
179.21
< 0.1%
165.11
< 0.1%
152.91
< 0.1%
144.91
< 0.1%
1431
< 0.1%
142.21
< 0.1%
136.91
< 0.1%
135.61
< 0.1%

Fibrinogen
Real number (ℝ≥0)

MISSING

Distinct674
Distinct (%)34.6%
Missing18052
Missing (%)90.3%
Infinite0
Infinite (%)0.0%
Mean293.2234343
Minimum35
Maximum1000
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size156.4 KiB
2021-11-29T11:24:02.640152image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/

Quantile statistics

Minimum35
5-th percentile133
Q1200
median258
Q3346.625
95-th percentile582.325
Maximum1000
Range965
Interquartile range (IQR)146.625

Descriptive statistics

Standard deviation143.5018304
Coefficient of variation (CV)0.4893941398
Kurtosis3.760735246
Mean293.2234343
Median Absolute Deviation (MAD)69.75
Skewness1.657971955
Sum571199.25
Variance20592.77533
MonotonicityNot monotonic
2021-11-29T11:24:02.744342image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
21718
 
0.1%
23015
 
0.1%
21514
 
0.1%
20013
 
0.1%
21912
 
0.1%
24711
 
0.1%
19011
 
0.1%
20811
 
0.1%
15411
 
0.1%
25011
 
0.1%
Other values (664)1821
 
9.1%
(Missing)18052
90.3%
ValueCountFrequency (%)
351
< 0.1%
55.51
< 0.1%
56.51
< 0.1%
59.51
< 0.1%
601
< 0.1%
612
< 0.1%
621
< 0.1%
641
< 0.1%
651
< 0.1%
701
< 0.1%
ValueCountFrequency (%)
10006
< 0.1%
9541
 
< 0.1%
9451
 
< 0.1%
9191
 
< 0.1%
9121
 
< 0.1%
8881
 
< 0.1%
884.51
 
< 0.1%
8821
 
< 0.1%
8781
 
< 0.1%
8671
 
< 0.1%

Platelets
Real number (ℝ≥0)

MISSING

Distinct1065
Distinct (%)5.9%
Missing1992
Missing (%)10.0%
Infinite0
Infinite (%)0.0%
Mean200.6354953
Minimum3
Maximum2322
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size156.4 KiB
2021-11-29T11:24:02.843999image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/

Quantile statistics

Minimum3
5-th percentile77.5
Q1139
median189
Q3247.625
95-th percentile360.5
Maximum2322
Range2319
Interquartile range (IQR)108.625

Descriptive statistics

Standard deviation93.09205897
Coefficient of variation (CV)0.4639859902
Kurtosis18.94566391
Mean200.6354953
Median Absolute Deviation (MAD)54
Skewness1.921419293
Sum3613044
Variance8666.131443
MonotonicityNot monotonic
2021-11-29T11:24:02.942836image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
20689
 
0.4%
18687
 
0.4%
15885
 
0.4%
14185
 
0.4%
17383
 
0.4%
18783
 
0.4%
18283
 
0.4%
16882
 
0.4%
16781
 
0.4%
16580
 
0.4%
Other values (1055)17170
85.9%
(Missing)1992
 
10.0%
ValueCountFrequency (%)
31
< 0.1%
42
< 0.1%
4.52
< 0.1%
52
< 0.1%
5.51
< 0.1%
71
< 0.1%
7.51
< 0.1%
82
< 0.1%
102
< 0.1%
10.51
< 0.1%
ValueCountFrequency (%)
23221
< 0.1%
10081
< 0.1%
9091
< 0.1%
893.51
< 0.1%
8661
< 0.1%
8551
< 0.1%
8541
< 0.1%
8381
< 0.1%
8341
< 0.1%
8311
< 0.1%

Age
Real number (ℝ≥0)

Distinct77
Distinct (%)0.4%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean60.6488
Minimum14
Maximum100
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size156.4 KiB
2021-11-29T11:24:03.043597image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/

Quantile statistics

Minimum14
5-th percentile30
Q150
median62
Q372
95-th percentile85
Maximum100
Range86
Interquartile range (IQR)22

Descriptive statistics

Standard deviation16.67181022
Coefficient of variation (CV)0.2748910155
Kurtosis-0.1562598942
Mean60.6488
Median Absolute Deviation (MAD)11
Skewness-0.2649819802
Sum1212976
Variance277.949256
MonotonicityNot monotonic
2021-11-29T11:24:03.137648image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
67572
 
2.9%
68539
 
2.7%
66512
 
2.6%
65510
 
2.5%
61498
 
2.5%
69495
 
2.5%
71481
 
2.4%
62477
 
2.4%
63470
 
2.4%
70467
 
2.3%
Other values (67)14979
74.9%
ValueCountFrequency (%)
142
 
< 0.1%
152
 
< 0.1%
165
 
< 0.1%
1713
 
0.1%
1832
 
0.2%
1954
0.3%
2065
0.3%
2199
0.5%
2259
0.3%
2377
0.4%
ValueCountFrequency (%)
100392
2.0%
89111
 
0.6%
88138
 
0.7%
87145
 
0.7%
86187
0.9%
85187
0.9%
84206
1.0%
83247
1.2%
82242
1.2%
81224
1.1%

Gender
Categorical

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size156.4 KiB
1.0
10732 
0.0
9268 

Length

Max length3
Median length3
Mean length3
Min length3

Characters and Unicode

Total characters60000
Distinct characters3
Distinct categories2 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row1.0
2nd row1.0
3rd row1.0
4th row1.0
5th row1.0

Common Values

ValueCountFrequency (%)
1.010732
53.7%
0.09268
46.3%

Length

2021-11-29T11:24:03.301855image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
Histogram of lengths of the category

Pie chart

2021-11-29T11:24:03.354340image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
ValueCountFrequency (%)
1.010732
53.7%
0.09268
46.3%

Most occurring characters

ValueCountFrequency (%)
029268
48.8%
.20000
33.3%
110732
 
17.9%

Most occurring categories

ValueCountFrequency (%)
Decimal Number40000
66.7%
Other Punctuation20000
33.3%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
029268
73.2%
110732
 
26.8%
Other Punctuation
ValueCountFrequency (%)
.20000
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common60000
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
029268
48.8%
.20000
33.3%
110732
 
17.9%

Most occurring blocks

ValueCountFrequency (%)
ASCII60000
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
029268
48.8%
.20000
33.3%
110732
 
17.9%

Unit1
Categorical

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
MISSING

Distinct2
Distinct (%)< 0.1%
Missing6095
Missing (%)30.5%
Memory size156.4 KiB
0.0
6982 
1.0
6923 

Length

Max length3
Median length3
Mean length3
Min length3

Characters and Unicode

Total characters41715
Distinct characters3
Distinct categories2 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row1.0
2nd row0.0
3rd row1.0
4th row1.0
5th row0.0

Common Values

ValueCountFrequency (%)
0.06982
34.9%
1.06923
34.6%
(Missing)6095
30.5%

Length

2021-11-29T11:24:03.408531image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
Histogram of lengths of the category

Pie chart

2021-11-29T11:24:03.459565image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
ValueCountFrequency (%)
0.06982
50.2%
1.06923
49.8%

Most occurring characters

ValueCountFrequency (%)
020887
50.1%
.13905
33.3%
16923
 
16.6%

Most occurring categories

ValueCountFrequency (%)
Decimal Number27810
66.7%
Other Punctuation13905
33.3%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
020887
75.1%
16923
 
24.9%
Other Punctuation
ValueCountFrequency (%)
.13905
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common41715
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
020887
50.1%
.13905
33.3%
16923
 
16.6%

Most occurring blocks

ValueCountFrequency (%)
ASCII41715
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
020887
50.1%
.13905
33.3%
16923
 
16.6%

Unit2
Categorical

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
MISSING

Distinct2
Distinct (%)< 0.1%
Missing6095
Missing (%)30.5%
Memory size156.4 KiB
1.0
6982 
0.0
6923 

Length

Max length3
Median length3
Mean length3
Min length3

Characters and Unicode

Total characters41715
Distinct characters3
Distinct categories2 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0.0
2nd row1.0
3rd row0.0
4th row0.0
5th row1.0

Common Values

ValueCountFrequency (%)
1.06982
34.9%
0.06923
34.6%
(Missing)6095
30.5%

Length

2021-11-29T11:24:03.513610image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
Histogram of lengths of the category

Pie chart

2021-11-29T11:24:03.564791image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
ValueCountFrequency (%)
1.06982
50.2%
0.06923
49.8%

Most occurring characters

ValueCountFrequency (%)
020828
49.9%
.13905
33.3%
16982
 
16.7%

Most occurring categories

ValueCountFrequency (%)
Decimal Number27810
66.7%
Other Punctuation13905
33.3%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
020828
74.9%
16982
 
25.1%
Other Punctuation
ValueCountFrequency (%)
.13905
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common41715
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
020828
49.9%
.13905
33.3%
16982
 
16.7%

Most occurring blocks

ValueCountFrequency (%)
ASCII41715
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
020828
49.9%
.13905
33.3%
16982
 
16.7%

HospAdmTime
Real number (ℝ)

ZEROS

Distinct7975
Distinct (%)39.9%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean-55.072586
Minimum-5366.86
Maximum0
Zeros1145
Zeros (%)5.7%
Negative18855
Negative (%)94.3%
Memory size156.4 KiB
2021-11-29T11:24:03.625391image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/

Quantile statistics

Minimum-5366.86
5-th percentile-244.829
Q1-52.3525
median-8.57
Q3-3.38
95-th percentile0
Maximum0
Range5366.86
Interquartile range (IQR)48.9725

Descriptive statistics

Standard deviation135.5956936
Coefficient of variation (CV)-2.462126867
Kurtosis241.4449376
Mean-55.072586
Median Absolute Deviation (MAD)8.52
Skewness-10.8324003
Sum-1101451.72
Variance18386.19213
MonotonicityNot monotonic
2021-11-29T11:24:03.729149image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
01145
 
5.7%
-0.02250
 
1.2%
-0.03197
 
1.0%
-0.01179
 
0.9%
-0.04136
 
0.7%
-0.05122
 
0.6%
-0.06105
 
0.5%
-0.0794
 
0.5%
-0.0976
 
0.4%
-0.0859
 
0.3%
Other values (7965)17637
88.2%
ValueCountFrequency (%)
-5366.861
< 0.1%
-3397.641
< 0.1%
-3342.341
< 0.1%
-3189.391
< 0.1%
-3112.121
< 0.1%
-2929.371
< 0.1%
-2842.111
< 0.1%
-2667.341
< 0.1%
-2384.781
< 0.1%
-2382.341
< 0.1%
ValueCountFrequency (%)
01145
5.7%
-0.01179
 
0.9%
-0.02250
 
1.2%
-0.03197
 
1.0%
-0.04136
 
0.7%
-0.05122
 
0.6%
-0.06105
 
0.5%
-0.0794
 
0.5%
-0.0859
 
0.3%
-0.0976
 
0.4%

ICULOS
Real number (ℝ≥0)

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION

Distinct229
Distinct (%)1.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean19.683325
Minimum4.5
Maximum170
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size156.4 KiB
2021-11-29T11:24:03.832754image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/

Quantile statistics

Minimum4.5
5-th percentile7.5
Q112
median20
Q324
95-th percentile29.5
Maximum170
Range165.5
Interquartile range (IQR)12

Descriptive statistics

Standard deviation11.64465905
Coefficient of variation (CV)0.5916002021
Kurtosis44.00773376
Mean19.683325
Median Absolute Deviation (MAD)6
Skewness4.964837969
Sum393666.5
Variance135.5980843
MonotonicityNot monotonic
2021-11-29T11:24:03.931570image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
20682
 
3.4%
19.5663
 
3.3%
20.5652
 
3.3%
18.5643
 
3.2%
21643
 
3.2%
22608
 
3.0%
21.5594
 
3.0%
19575
 
2.9%
22.5570
 
2.9%
11558
 
2.8%
Other values (219)13812
69.1%
ValueCountFrequency (%)
4.5193
1.0%
5102
 
0.5%
5.5115
 
0.6%
6105
 
0.5%
6.5145
 
0.7%
7176
0.9%
7.5202
1.0%
8294
1.5%
8.5325
1.6%
9375
1.9%
ValueCountFrequency (%)
1701
 
< 0.1%
1691
 
< 0.1%
168.55
< 0.1%
1681
 
< 0.1%
167.51
 
< 0.1%
1641
 
< 0.1%
163.51
 
< 0.1%
160.51
 
< 0.1%
159.51
 
< 0.1%
156.52
 
< 0.1%

SepsisLabel
Categorical

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION

Distinct3
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size156.4 KiB
0.0
19615 
1.0
 
376
0.5
 
9

Length

Max length3
Median length3
Mean length3
Min length3

Characters and Unicode

Total characters60000
Distinct characters4
Distinct categories2 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0.0
2nd row0.0
3rd row0.0
4th row0.0
5th row0.0

Common Values

ValueCountFrequency (%)
0.019615
98.1%
1.0376
 
1.9%
0.59
 
< 0.1%

Length

2021-11-29T11:24:04.028725image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
Histogram of lengths of the category

Pie chart

2021-11-29T11:24:04.083599image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
ValueCountFrequency (%)
0.019615
98.1%
1.0376
 
1.9%
0.59
 
< 0.1%

Most occurring characters

ValueCountFrequency (%)
039615
66.0%
.20000
33.3%
1376
 
0.6%
59
 
< 0.1%

Most occurring categories

ValueCountFrequency (%)
Decimal Number40000
66.7%
Other Punctuation20000
33.3%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
039615
99.0%
1376
 
0.9%
59
 
< 0.1%
Other Punctuation
ValueCountFrequency (%)
.20000
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common60000
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
039615
66.0%
.20000
33.3%
1376
 
0.6%
59
 
< 0.1%

Most occurring blocks

ValueCountFrequency (%)
ASCII60000
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
039615
66.0%
.20000
33.3%
1376
 
0.6%
59
 
< 0.1%

Sepsis
Categorical

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size156.4 KiB
0.0
18858 
1.0
 
1142

Length

Max length3
Median length3
Mean length3
Min length3

Characters and Unicode

Total characters60000
Distinct characters3
Distinct categories2 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0.0
2nd row0.0
3rd row0.0
4th row0.0
5th row0.0

Common Values

ValueCountFrequency (%)
0.018858
94.3%
1.01142
 
5.7%

Length

2021-11-29T11:24:04.144291image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
Histogram of lengths of the category

Pie chart

2021-11-29T11:24:04.196995image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
ValueCountFrequency (%)
0.018858
94.3%
1.01142
 
5.7%

Most occurring characters

ValueCountFrequency (%)
038858
64.8%
.20000
33.3%
11142
 
1.9%

Most occurring categories

ValueCountFrequency (%)
Decimal Number40000
66.7%
Other Punctuation20000
33.3%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
038858
97.1%
11142
 
2.9%
Other Punctuation
ValueCountFrequency (%)
.20000
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common60000
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
038858
64.8%
.20000
33.3%
11142
 
1.9%

Most occurring blocks

ValueCountFrequency (%)
ASCII60000
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
038858
64.8%
.20000
33.3%
11142
 
1.9%

Hours
Real number (ℝ≥0)

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION

Distinct230
Distinct (%)1.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean38.09975
Minimum8
Maximum336
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size156.4 KiB
2021-11-29T11:24:04.259190image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/

Quantile statistics

Minimum8
5-th percentile14
Q123
median38
Q347
95-th percentile58
Maximum336
Range328
Interquartile range (IQR)24

Descriptive statistics

Standard deviation23.27525267
Coefficient of variation (CV)0.6109030287
Kurtosis44.03440685
Mean38.09975
Median Absolute Deviation (MAD)12
Skewness4.965291886
Sum761995
Variance541.7373868
MonotonicityNot monotonic
2021-11-29T11:24:04.358442image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
39697
 
3.5%
38667
 
3.3%
36664
 
3.3%
40651
 
3.3%
41639
 
3.2%
43610
 
3.0%
42596
 
3.0%
37591
 
3.0%
21564
 
2.8%
44563
 
2.8%
Other values (220)13758
68.8%
ValueCountFrequency (%)
8204
1.0%
9114
 
0.6%
10126
 
0.6%
11120
 
0.6%
12164
0.8%
13205
1.0%
14218
1.1%
15305
1.5%
16327
1.6%
17373
1.9%
ValueCountFrequency (%)
3365
< 0.1%
3352
 
< 0.1%
3341
 
< 0.1%
3331
 
< 0.1%
3271
 
< 0.1%
3261
 
< 0.1%
3201
 
< 0.1%
3181
 
< 0.1%
3122
 
< 0.1%
3102
 
< 0.1%

Interactions

2021-11-29T11:23:52.775347image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2021-11-29T11:23:49.413289image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2021-11-29T11:23:49.513047image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2021-11-29T11:23:49.604035image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2021-11-29T11:23:49.694875image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2021-11-29T11:23:49.779539image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2021-11-29T11:23:49.870167image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2021-11-29T11:23:49.956778image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2021-11-29T11:23:50.042767image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2021-11-29T11:23:50.126812image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2021-11-29T11:23:50.209563image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2021-11-29T11:23:50.294117image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2021-11-29T11:23:50.373292image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2021-11-29T11:23:50.466170image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2021-11-29T11:23:50.549373image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2021-11-29T11:23:50.632306image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2021-11-29T11:23:50.717333image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2021-11-29T11:23:50.802245image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2021-11-29T11:23:50.892834image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2021-11-29T11:23:50.983537image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2021-11-29T11:23:51.068172image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2021-11-29T11:23:51.224520image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2021-11-29T11:23:51.308845image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2021-11-29T11:23:51.394289image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2021-11-29T11:23:51.486336image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2021-11-29T11:23:51.570334image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2021-11-29T11:23:51.658947image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2021-11-29T11:23:51.745119image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2021-11-29T11:23:51.828516image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2021-11-29T11:23:51.914878image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2021-11-29T11:23:51.999892image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2021-11-29T11:23:52.085352image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2021-11-29T11:23:52.167733image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2021-11-29T11:23:52.251683image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2021-11-29T11:23:52.337740image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2021-11-29T11:23:52.425314image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2021-11-29T11:23:52.511108image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2021-11-29T11:23:52.594858image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2021-11-29T11:23:52.685053image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/

Correlations

2021-11-29T11:24:04.502592image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/

Spearman's ρ

The Spearman's rank correlation coefficient (ρ) is a measure of monotonic correlation between two variables, and is therefore better in catching nonlinear monotonic correlations than Pearson's r. It's value lies between -1 and +1, -1 indicating total negative monotonic correlation, 0 indicating no monotonic correlation and 1 indicating total positive monotonic correlation.

To calculate ρ for two variables X and Y, one divides the covariance of the rank variables of X and Y by the product of their standard deviations.
2021-11-29T11:24:04.912590image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/

Pearson's r

The Pearson's correlation coefficient (r) is a measure of linear correlation between two variables. It's value lies between -1 and +1, -1 indicating total negative linear correlation, 0 indicating no linear correlation and 1 indicating total positive linear correlation. Furthermore, r is invariant under separate changes in location and scale of the two variables, implying that for a linear function the angle to the x-axis does not affect r.

To calculate r for two variables X and Y, one divides the covariance of X and Y by the product of their standard deviations.
2021-11-29T11:24:05.246375image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/

Kendall's τ

Similarly to Spearman's rank correlation coefficient, the Kendall rank correlation coefficient (τ) measures ordinal association between two variables. It's value lies between -1 and +1, -1 indicating total negative correlation, 0 indicating no correlation and 1 indicating total positive correlation.

To calculate τ for two variables X and Y, one determines the number of concordant and discordant pairs of observations. τ is given by the number of concordant pairs minus the discordant pairs divided by the total number of pairs.
2021-11-29T11:24:05.525253image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/

Cramér's V (φc)

Cramér's V is an association measure for nominal random variables. The coefficient ranges from 0 to 1, with 0 indicating independence and 1 indicating perfect association. The empirical estimators used for Cramér's V have been proved to be biased, even for large samples. We use a bias-corrected measure that has been proposed by Bergsma in 2013 that can be found here.

Missing values

2021-11-29T11:23:53.090200image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
A simple visualization of nullity by column.
2021-11-29T11:23:54.136376image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
Nullity matrix is a data-dense display which lets you quickly visually pick out patterns in data completion.
2021-11-29T11:23:54.765382image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
The correlation heatmap measures nullity correlation: how strongly the presence or absence of one variable affects the presence of another.
2021-11-29T11:23:55.548491image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
The dendrogram allows you to more fully correlate variable completion, revealing trends deeper than the pairwise ones visible in the correlation heatmap.

Sample

First rows

PatientIDHRO2SatTempSBPMAPDBPRespEtCO2BaseExcessHCO3FiO2pHPaCO2SaO2ASTBUNAlkalinephosCalciumChlorideCreatinineBilirubin_directGlucoseLactateMagnesiumPhosphatePotassiumBilirubin_totalTroponinIHctHgbPTTWBCFibrinogenPlateletsAgeGenderUnit1Unit2HospAdmTimeICULOSSepsisLabelSepsisHours
010000198.0095.036.800112.084.063.0020.00NaNNaNNaNNaNNaNNaNNaNNaN30.0NaN7.80NaN1.500NaN123.0NaN2.05NaN3.95NaNNaN35.3011.3NaN10.80NaN170.073.01.01.00.0-214.6412.50.00.024.0
110000264.0098.037.500129.083.062.0022.0037.5NaNNaNNaNNaNNaNNaNNaN17.0NaN8.20NaN0.840NaN113.5NaN2.252.704.70NaN3.70031.4011.1NaN13.20NaN85.083.01.00.01.0-123.1713.00.00.025.0
2100003114.0094.036.950131.094.082.0025.50NaNNaNNaNNaNNaNNaNNaN5597.036.0337.08.30NaN1.8903.35123.5NaN2.304.304.704.250.60033.3511.1NaN14.05NaN297.548.01.0NaNNaN-2.8322.00.00.043.0
310000485.00100.037.700132.084.065.0012.00NaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaN67.01.01.00.0-1.9330.00.00.059.0
410000569.0097.536.300137.075.055.0024.00NaNNaNNaN0.47.53523.994.0540.055.0231.08.70NaN1.180NaN145.02.7751.954.505.200.600.03525.308.532.315.80NaN365.050.01.01.00.0-3.2726.50.00.052.0
510000671.0095.036.525145.097.074.0017.00NaNNaN23.8NaNNaN39.598.6538.017.051.08.60NaN0.710NaN222.5NaN2.055.104.401.10NaN47.0015.924.27.70NaN148.050.01.0NaNNaN0.0029.00.00.047.0
610000789.0097.037.200145.092.571.5018.00NaNNaNNaNNaNNaNNaNNaN34.015.5112.08.45NaN0.4050.10128.0NaN1.704.003.900.50NaN26.408.226.67.00NaN403.042.01.0NaNNaN-1145.9719.00.00.037.0
710000879.7597.036.650148.081.058.0018.0034.0NaNNaN0.47.40033.0NaNNaN42.5NaN7.90104.03.020NaN123.01.8302.404.155.40NaNNaN22.207.2NaN11.60NaN110.065.01.00.01.0-211.6425.50.00.050.0
810000967.0098.036.450140.083.564.2517.75NaNNaNNaNNaNNaNNaNNaNNaN12.0NaN9.30NaN0.710NaN103.0NaN1.90NaN3.90NaNNaN36.2011.8NaN5.70NaN201.082.00.0NaNNaN-128.3915.50.00.030.0
910001091.0099.036.800148.098.084.0016.00NaNNaNNaNNaNNaNNaNNaNNaN34.0NaN9.00NaN1.770NaN186.0NaN2.204.104.10NaNNaNNaNNaNNaNNaNNaNNaN32.00.01.00.0-8.138.50.00.016.0

Last rows

PatientIDHRO2SatTempSBPMAPDBPRespEtCO2BaseExcessHCO3FiO2pHPaCO2SaO2ASTBUNAlkalinephosCalciumChlorideCreatinineBilirubin_directGlucoseLactateMagnesiumPhosphatePotassiumBilirubin_totalTroponinIHctHgbPTTWBCFibrinogenPlateletsAgeGenderUnit1Unit2HospAdmTimeICULOSSepsisLabelSepsisHours
1999011999176.097.037.00130.0082.052.016.035.0NaNNaNNaNNaNNaNNaNNaN14.0NaN7.50NaN0.790NaN110.00NaN1.60NaN4.10NaNNaN26.308.40NaN6.4NaN96.081.00.00.01.0-66.1313.00.00.025.0
1999111999291.096.036.80144.50100.073.020.0NaNNaNNaNNaNNaNNaNNaNNaN37.0NaN9.20NaN9.930NaN103.00NaN1.806.64.50NaN0.4130.209.50NaN2.8NaN198.045.01.01.00.0-4.5521.00.00.041.0
1999211999381.095.036.50130.0097.077.519.0NaNNaNNaNNaNNaNNaNNaNNaN15.0NaN8.30NaN1.010NaN132.00NaN2.002.64.10NaN0.0142.0014.90NaN12.3NaN175.065.01.0NaNNaN-3.5311.00.00.021.0
1999311999474.096.037.50122.0076.056.018.028.0NaNNaN0.47.29534.598.55NaN16.0NaN1.26NaN1.060NaN135.506.662.003.64.05NaNNaN30.3010.25NaN9.3NaN66.571.01.00.01.0-29.5721.50.00.042.0
1999411999562.095.036.05147.00107.079.020.0NaNNaNNaNNaNNaNNaNNaNNaN9.0NaN8.80NaN0.810NaN116.00NaN2.003.03.50NaNNaN39.2013.10NaN7.0289.0154.076.01.00.01.0-14.9021.50.00.042.0
1999511999685.598.036.50130.0085.072.019.0NaNNaNNaNNaNNaNNaNNaN849.06.0259.08.75NaN0.495NaN147.50NaN1.95NaN3.653.30.0142.7013.80NaN12.6NaN238.084.00.0NaNNaN-6.6924.50.00.048.0
1999611999761.597.036.80117.5086.069.022.045.0NaNNaNNaNNaNNaNNaN24.05.5116.013.80NaN0.7700.1103.75NaN3.153.13.350.71.0946.7015.5538.210.4NaN189.030.01.0NaNNaN-0.0213.00.00.025.0
1999711999881.598.036.70155.75114.087.021.0NaNNaNNaNNaNNaNNaNNaN9.055.068.08.05NaN7.685NaN87.00NaN1.904.14.250.2NaN27.608.20NaN12.5NaN188.060.00.01.00.0-53.6425.00.00.049.0
1999811999994.093.037.60140.00100.075.022.0NaNNaNNaNNaNNaNNaNNaN33.528.549.58.45NaN1.010NaN109.50NaNNaNNaN3.401.0NaN23.608.00NaN10.7NaN263.584.00.01.00.0-10.7410.50.00.020.0
1999912000082.098.036.70123.0090.070.016.0NaNNaNNaNNaNNaNNaNNaN18.010.075.09.10NaN0.5350.1192.00NaN2.204.03.450.9NaN37.3511.8029.16.1NaN223.062.00.0NaNNaN0.0018.00.00.035.0